Overview

Dataset statistics

Number of variables24
Number of observations3669
Missing cells21
Missing cells (%)< 0.1%
Duplicate rows1272
Duplicate rows (%)34.7%
Total size in memory688.1 KiB
Average record size in memory192.0 B

Variable types

Numeric20
Categorical4

Alerts

Dataset has 1272 (34.7%) duplicate rowsDuplicates
PAY_0 is highly overall correlated with PAY_2High correlation
PAY_2 is highly overall correlated with PAY_0 and 9 other fieldsHigh correlation
PAY_3 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
PAY_4 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
PAY_5 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
PAY_6 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
BILL_AMT1 is highly overall correlated with PAY_2 and 10 other fieldsHigh correlation
BILL_AMT2 is highly overall correlated with PAY_2 and 11 other fieldsHigh correlation
BILL_AMT3 is highly overall correlated with PAY_2 and 14 other fieldsHigh correlation
BILL_AMT4 is highly overall correlated with PAY_2 and 13 other fieldsHigh correlation
BILL_AMT5 is highly overall correlated with PAY_2 and 14 other fieldsHigh correlation
BILL_AMT6 is highly overall correlated with PAY_3 and 13 other fieldsHigh correlation
PAY_AMT1 is highly overall correlated with BILL_AMT1 and 3 other fieldsHigh correlation
PAY_AMT2 is highly overall correlated with BILL_AMT3 and 7 other fieldsHigh correlation
PAY_AMT3 is highly overall correlated with BILL_AMT2 and 9 other fieldsHigh correlation
PAY_AMT4 is highly overall correlated with BILL_AMT3 and 7 other fieldsHigh correlation
PAY_AMT5 is highly overall correlated with BILL_AMT5 and 5 other fieldsHigh correlation
PAY_AMT6 is highly overall correlated with BILL_AMT3 and 7 other fieldsHigh correlation
SEX is highly overall correlated with EDUCATION and 1 other fieldsHigh correlation
EDUCATION is highly overall correlated with SEX and 1 other fieldsHigh correlation
MARRIAGE is highly overall correlated with SEX and 1 other fieldsHigh correlation
MARRIAGE is highly imbalanced (52.2%)Imbalance
PAY_AMT3 is highly skewed (γ1 = 28.75821047)Skewed
PAY_0 has 1741 (47.5%) zerosZeros
PAY_2 has 1901 (51.8%) zerosZeros
PAY_3 has 1875 (51.1%) zerosZeros
PAY_4 has 1995 (54.4%) zerosZeros
PAY_5 has 1996 (54.4%) zerosZeros
PAY_6 has 1879 (51.2%) zerosZeros
BILL_AMT1 has 244 (6.7%) zerosZeros
BILL_AMT2 has 328 (8.9%) zerosZeros
BILL_AMT3 has 384 (10.5%) zerosZeros
BILL_AMT4 has 424 (11.6%) zerosZeros
BILL_AMT5 has 460 (12.5%) zerosZeros
BILL_AMT6 has 532 (14.5%) zerosZeros
PAY_AMT1 has 667 (18.2%) zerosZeros
PAY_AMT2 has 708 (19.3%) zerosZeros
PAY_AMT3 has 798 (21.7%) zerosZeros
PAY_AMT4 has 808 (22.0%) zerosZeros
PAY_AMT5 has 827 (22.5%) zerosZeros
PAY_AMT6 has 949 (25.9%) zerosZeros

Reproduction

Analysis started2023-12-15 01:58:52.911555
Analysis finished2023-12-15 01:59:09.510878
Duration16.6 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ)

Distinct62
Distinct (%)1.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean166284.08
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:59:09.547811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3230000
95-th percentile420000
Maximum1000000
Range990000
Interquartile range (IQR)180000

Descriptive statistics

Standard deviation129512.16
Coefficient of variation (CV)0.77886083
Kurtosis1.0085552
Mean166284.08
Median Absolute Deviation (MAD)90000
Skewness1.0735486
Sum6.0993 × 108
Variance1.6773398 × 1010
MonotonicityNot monotonic
2023-12-14T19:59:09.589917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 453
 
12.3%
20000 236
 
6.4%
30000 191
 
5.2%
200000 182
 
5.0%
80000 165
 
4.5%
180000 135
 
3.7%
360000 122
 
3.3%
100000 118
 
3.2%
140000 117
 
3.2%
150000 115
 
3.1%
Other values (52) 1834
50.0%
ValueCountFrequency (%)
10000 53
 
1.4%
20000 236
6.4%
30000 191
5.2%
40000 30
 
0.8%
50000 453
12.3%
60000 98
 
2.7%
70000 91
 
2.5%
80000 165
 
4.5%
90000 96
 
2.6%
100000 118
 
3.2%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
700000 2
 
0.1%
680000 2
 
0.1%
670000 2
 
0.1%
630000 5
0.1%
620000 2
 
0.1%
610000 2
 
0.1%
600000 3
0.1%
580000 5
0.1%
550000 2
 
0.1%

SEX
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
female
2130 
male
1538 
SEX
 
1

Length

Max length6
Median length6
Mean length5.1608068
Min length3

Characters and Unicode

Total characters18935
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
female 2130
58.1%
male 1538
41.9%
SEX 1
 
< 0.1%

Length

2023-12-14T19:59:09.626619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T19:59:09.662170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
female 2130
58.1%
male 1538
41.9%
sex 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 5798
30.6%
m 3668
19.4%
a 3668
19.4%
l 3668
19.4%
f 2130
 
11.2%
S 1
 
< 0.1%
E 1
 
< 0.1%
X 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18932
> 99.9%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5798
30.6%
m 3668
19.4%
a 3668
19.4%
l 3668
19.4%
f 2130
 
11.3%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
E 1
33.3%
X 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 18935
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5798
30.6%
m 3668
19.4%
a 3668
19.4%
l 3668
19.4%
f 2130
 
11.2%
S 1
 
< 0.1%
E 1
 
< 0.1%
X 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5798
30.6%
m 3668
19.4%
a 3668
19.4%
l 3668
19.4%
f 2130
 
11.2%
S 1
 
< 0.1%
E 1
 
< 0.1%
X 1
 
< 0.1%

EDUCATION
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
university
1644 
graduate school
1401 
high school
596 
other
 
27
EDUCATION
 
1

Length

Max length15
Median length11
Mean length12.034614
Min length5

Characters and Unicode

Total characters44155
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowuniversity
2nd rowuniversity
3rd rowuniversity
4th rowuniversity
5th rowuniversity

Common Values

ValueCountFrequency (%)
university 1644
44.8%
graduate school 1401
38.2%
high school 596
 
16.2%
other 27
 
0.7%
EDUCATION 1
 
< 0.1%

Length

2023-12-14T19:59:09.693537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T19:59:09.727552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
school 1997
35.2%
university 1644
29.0%
graduate 1401
24.7%
high 596
 
10.5%
other 27
 
0.5%
education 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 4021
 
9.1%
i 3884
 
8.8%
s 3641
 
8.2%
h 3216
 
7.3%
e 3072
 
7.0%
r 3072
 
7.0%
t 3072
 
7.0%
u 3045
 
6.9%
a 2802
 
6.3%
1997
 
4.5%
Other values (16) 12333
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42149
95.5%
Space Separator 1997
 
4.5%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4021
 
9.5%
i 3884
 
9.2%
s 3641
 
8.6%
h 3216
 
7.6%
e 3072
 
7.3%
r 3072
 
7.3%
t 3072
 
7.3%
u 3045
 
7.2%
a 2802
 
6.6%
l 1997
 
4.7%
Other values (6) 10327
24.5%
Uppercase Letter
ValueCountFrequency (%)
E 1
11.1%
D 1
11.1%
U 1
11.1%
C 1
11.1%
A 1
11.1%
T 1
11.1%
I 1
11.1%
O 1
11.1%
N 1
11.1%
Space Separator
ValueCountFrequency (%)
1997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42158
95.5%
Common 1997
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4021
 
9.5%
i 3884
 
9.2%
s 3641
 
8.6%
h 3216
 
7.6%
e 3072
 
7.3%
r 3072
 
7.3%
t 3072
 
7.3%
u 3045
 
7.2%
a 2802
 
6.6%
l 1997
 
4.7%
Other values (15) 10336
24.5%
Common
ValueCountFrequency (%)
1997
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4021
 
9.1%
i 3884
 
8.8%
s 3641
 
8.2%
h 3216
 
7.3%
e 3072
 
7.0%
r 3072
 
7.0%
t 3072
 
7.0%
u 3045
 
6.9%
a 2802
 
6.3%
1997
 
4.5%
Other values (16) 12333
27.9%

MARRIAGE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2
2045 
1
1559 
3
 
54
0
 
10
MARRIAGE
 
1

Length

Max length8
Median length1
Mean length1.0019079
Min length1

Characters and Unicode

Total characters3676
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 2045
55.7%
1 1559
42.5%
3 54
 
1.5%
0 10
 
0.3%
MARRIAGE 1
 
< 0.1%

Length

2023-12-14T19:59:09.765988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T19:59:09.803105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2 2045
55.7%
1 1559
42.5%
3 54
 
1.5%
0 10
 
0.3%
marriage 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 2045
55.6%
1 1559
42.4%
3 54
 
1.5%
0 10
 
0.3%
A 2
 
0.1%
R 2
 
0.1%
M 1
 
< 0.1%
I 1
 
< 0.1%
G 1
 
< 0.1%
E 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3668
99.8%
Uppercase Letter 8
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2
25.0%
R 2
25.0%
M 1
12.5%
I 1
12.5%
G 1
12.5%
E 1
12.5%
Decimal Number
ValueCountFrequency (%)
2 2045
55.8%
1 1559
42.5%
3 54
 
1.5%
0 10
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3668
99.8%
Latin 8
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2
25.0%
R 2
25.0%
M 1
12.5%
I 1
12.5%
G 1
12.5%
E 1
12.5%
Common
ValueCountFrequency (%)
2 2045
55.8%
1 1559
42.5%
3 54
 
1.5%
0 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2045
55.6%
1 1559
42.4%
3 54
 
1.5%
0 10
 
0.3%
A 2
 
0.1%
R 2
 
0.1%
M 1
 
< 0.1%
I 1
 
< 0.1%
G 1
 
< 0.1%
E 1
 
< 0.1%

AGE
Real number (ℝ)

Distinct52
Distinct (%)1.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35.354144
Minimum21
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:59:09.838328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum75
Range54
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4381712
Coefficient of variation (CV)0.26696082
Kurtosis0.23723543
Mean35.354144
Median Absolute Deviation (MAD)6
Skewness0.80637489
Sum129679
Variance89.079075
MonotonicityNot monotonic
2023-12-14T19:59:09.879604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 214
 
5.8%
27 185
 
5.0%
30 174
 
4.7%
26 158
 
4.3%
24 155
 
4.2%
32 152
 
4.1%
34 151
 
4.1%
28 147
 
4.0%
31 145
 
4.0%
35 135
 
3.7%
Other values (42) 2052
55.9%
ValueCountFrequency (%)
21 7
 
0.2%
22 90
2.5%
23 124
3.4%
24 155
4.2%
25 131
3.6%
26 158
4.3%
27 185
5.0%
28 147
4.0%
29 214
5.8%
30 174
4.7%
ValueCountFrequency (%)
75 2
 
0.1%
73 2
 
0.1%
72 1
 
< 0.1%
71 1
 
< 0.1%
70 2
 
0.1%
67 5
0.1%
66 6
0.2%
65 2
 
0.1%
64 1
 
< 0.1%
63 4
0.1%

PAY_0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.00081788441
Minimum-2
Maximum8
Zeros1741
Zeros (%)47.5%
Negative1061
Negative (%)28.9%
Memory size28.8 KiB
2023-12-14T19:59:09.913086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.130073
Coefficient of variation (CV)1381.7026
Kurtosis6.1231377
Mean0.00081788441
Median Absolute Deviation (MAD)1
Skewness1.1940289
Sum3
Variance1.2770651
MonotonicityNot monotonic
2023-12-14T19:59:09.942523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1741
47.5%
-1 786
21.4%
1 495
 
13.5%
2 327
 
8.9%
-2 275
 
7.5%
3 25
 
0.7%
4 9
 
0.2%
8 9
 
0.2%
7 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 275
 
7.5%
-1 786
21.4%
0 1741
47.5%
1 495
 
13.5%
2 327
 
8.9%
3 25
 
0.7%
4 9
 
0.2%
7 1
 
< 0.1%
8 9
 
0.2%
ValueCountFrequency (%)
8 9
 
0.2%
7 1
 
< 0.1%
4 9
 
0.2%
3 25
 
0.7%
2 327
 
8.9%
1 495
 
13.5%
0 1741
47.5%
-1 786
21.4%
-2 275
 
7.5%

PAY_2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.13631407
Minimum-2
Maximum7
Zeros1901
Zeros (%)51.8%
Negative1229
Negative (%)33.5%
Memory size28.8 KiB
2023-12-14T19:59:09.972229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2071782
Coefficient of variation (CV)-8.8558594
Kurtosis3.1275791
Mean-0.13631407
Median Absolute Deviation (MAD)0
Skewness1.0287184
Sum-500
Variance1.4572792
MonotonicityNot monotonic
2023-12-14T19:59:10.001631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 1901
51.8%
-1 787
21.4%
2 487
 
13.3%
-2 442
 
12.0%
3 31
 
0.8%
7 9
 
0.2%
4 4
 
0.1%
1 3
 
0.1%
5 2
 
0.1%
6 2
 
0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 442
 
12.0%
-1 787
21.4%
0 1901
51.8%
1 3
 
0.1%
2 487
 
13.3%
3 31
 
0.8%
4 4
 
0.1%
5 2
 
0.1%
6 2
 
0.1%
7 9
 
0.2%
ValueCountFrequency (%)
7 9
 
0.2%
6 2
 
0.1%
5 2
 
0.1%
4 4
 
0.1%
3 31
 
0.8%
2 487
 
13.3%
1 3
 
0.1%
0 1901
51.8%
-1 787
21.4%
-2 442
 
12.0%

PAY_3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.1540349
Minimum-2
Maximum7
Zeros1875
Zeros (%)51.1%
Negative1272
Negative (%)34.7%
Memory size28.8 KiB
2023-12-14T19:59:10.031478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2546038
Coefficient of variation (CV)-8.1449321
Kurtosis4.1195494
Mean-0.1540349
Median Absolute Deviation (MAD)0
Skewness1.2639102
Sum-565
Variance1.5740306
MonotonicityNot monotonic
2023-12-14T19:59:10.058820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 1875
51.1%
-1 797
21.7%
-2 475
 
12.9%
2 468
 
12.8%
4 14
 
0.4%
3 11
 
0.3%
7 10
 
0.3%
6 9
 
0.2%
5 6
 
0.2%
1 3
 
0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 475
 
12.9%
-1 797
21.7%
0 1875
51.1%
1 3
 
0.1%
2 468
 
12.8%
3 11
 
0.3%
4 14
 
0.4%
5 6
 
0.2%
6 9
 
0.2%
7 10
 
0.3%
ValueCountFrequency (%)
7 10
 
0.3%
6 9
 
0.2%
5 6
 
0.2%
4 14
 
0.4%
3 11
 
0.3%
2 468
 
12.8%
1 3
 
0.1%
0 1875
51.1%
-1 797
21.7%
-2 475
 
12.9%

PAY_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.25436205
Minimum-2
Maximum7
Zeros1995
Zeros (%)54.4%
Negative1291
Negative (%)35.2%
Memory size28.8 KiB
2023-12-14T19:59:10.087297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.183663
Coefficient of variation (CV)-4.6534576
Kurtosis4.7005083
Mean-0.25436205
Median Absolute Deviation (MAD)0
Skewness1.2605956
Sum-933
Variance1.4010581
MonotonicityNot monotonic
2023-12-14T19:59:10.112931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1995
54.4%
-1 753
 
20.5%
-2 538
 
14.7%
2 322
 
8.8%
3 29
 
0.8%
5 12
 
0.3%
4 9
 
0.2%
7 9
 
0.2%
6 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 538
 
14.7%
-1 753
 
20.5%
0 1995
54.4%
2 322
 
8.8%
3 29
 
0.8%
4 9
 
0.2%
5 12
 
0.3%
6 1
 
< 0.1%
7 9
 
0.2%
ValueCountFrequency (%)
7 9
 
0.2%
6 1
 
< 0.1%
5 12
 
0.3%
4 9
 
0.2%
3 29
 
0.8%
2 322
 
8.8%
0 1995
54.4%
-1 753
 
20.5%
-2 538
 
14.7%

PAY_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.2674482
Minimum-2
Maximum7
Zeros1996
Zeros (%)54.4%
Negative1296
Negative (%)35.3%
Memory size28.8 KiB
2023-12-14T19:59:10.140058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1649636
Coefficient of variation (CV)-4.3558477
Kurtosis4.6026359
Mean-0.2674482
Median Absolute Deviation (MAD)0
Skewness1.1873709
Sum-981
Variance1.3571403
MonotonicityNot monotonic
2023-12-14T19:59:10.165360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1996
54.4%
-1 749
 
20.4%
-2 547
 
14.9%
2 328
 
8.9%
3 18
 
0.5%
4 18
 
0.5%
7 10
 
0.3%
5 2
 
0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 547
 
14.9%
-1 749
 
20.4%
0 1996
54.4%
2 328
 
8.9%
3 18
 
0.5%
4 18
 
0.5%
5 2
 
0.1%
7 10
 
0.3%
ValueCountFrequency (%)
7 10
 
0.3%
5 2
 
0.1%
4 18
 
0.5%
3 18
 
0.5%
2 328
 
8.9%
0 1996
54.4%
-1 749
 
20.4%
-2 547
 
14.9%

PAY_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.29389313
Minimum-2
Maximum8
Zeros1879
Zeros (%)51.2%
Negative1393
Negative (%)38.0%
Memory size28.8 KiB
2023-12-14T19:59:10.193831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1900395
Coefficient of variation (CV)-4.0492252
Kurtosis4.3412627
Mean-0.29389313
Median Absolute Deviation (MAD)0
Skewness1.1879815
Sum-1078
Variance1.4161939
MonotonicityNot monotonic
2023-12-14T19:59:10.224318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1879
51.2%
-1 810
22.1%
-2 583
 
15.9%
2 347
 
9.5%
3 30
 
0.8%
6 8
 
0.2%
7 6
 
0.2%
4 4
 
0.1%
8 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
-2 583
 
15.9%
-1 810
22.1%
0 1879
51.2%
2 347
 
9.5%
3 30
 
0.8%
4 4
 
0.1%
6 8
 
0.2%
7 6
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 6
 
0.2%
6 8
 
0.2%
4 4
 
0.1%
3 30
 
0.8%
2 347
 
9.5%
0 1879
51.2%
-1 810
22.1%
-2 583
 
15.9%

BILL_AMT1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2137
Distinct (%)58.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean51054.57
Minimum-14386
Maximum964511
Zeros244
Zeros (%)6.7%
Negative83
Negative (%)2.3%
Memory size28.8 KiB
2023-12-14T19:59:10.260508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-14386
5-th percentile0
Q13096
median21148
Q363638.75
95-th percentile203183
Maximum964511
Range978897
Interquartile range (IQR)60542.75

Descriptive statistics

Standard deviation76373.892
Coefficient of variation (CV)1.4959266
Kurtosis13.444545
Mean51054.57
Median Absolute Deviation (MAD)20735.5
Skewness2.9687536
Sum1.8726816 × 108
Variance5.8329714 × 109
MonotonicityNot monotonic
2023-12-14T19:59:10.301583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 244
 
6.7%
390 31
 
0.8%
780 12
 
0.3%
316 11
 
0.3%
396 10
 
0.3%
-200 8
 
0.2%
291 7
 
0.2%
2400 7
 
0.2%
1261 6
 
0.2%
326 6
 
0.2%
Other values (2127) 3326
90.7%
ValueCountFrequency (%)
-14386 2
0.1%
-2000 3
0.1%
-1886 1
 
< 0.1%
-1540 2
0.1%
-1312 2
0.1%
-1100 2
0.1%
-984 1
 
< 0.1%
-946 2
0.1%
-819 1
 
< 0.1%
-800 1
 
< 0.1%
ValueCountFrequency (%)
964511 1
< 0.1%
546741 1
< 0.1%
507726 2
0.1%
507062 2
0.1%
495559 1
< 0.1%
485921 1
< 0.1%
482250 1
< 0.1%
471814 2
0.1%
467150 2
0.1%
459600 1
< 0.1%

BILL_AMT2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2091
Distinct (%)57.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean49166.293
Minimum-24704
Maximum983931
Zeros328
Zeros (%)8.9%
Negative92
Negative (%)2.5%
Memory size28.8 KiB
2023-12-14T19:59:10.342529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-24704
5-th percentile0
Q13123
median20417
Q361162
95-th percentile196143
Maximum983931
Range1008635
Interquartile range (IQR)58039

Descriptive statistics

Standard deviation74703.121
Coefficient of variation (CV)1.5193971
Kurtosis15.094803
Mean49166.293
Median Absolute Deviation (MAD)20089
Skewness3.1001486
Sum1.8034196 × 108
Variance5.5805563 × 109
MonotonicityNot monotonic
2023-12-14T19:59:10.384156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 328
 
8.9%
390 17
 
0.5%
-200 14
 
0.4%
316 13
 
0.4%
291 10
 
0.3%
780 9
 
0.2%
326 8
 
0.2%
300 8
 
0.2%
396 7
 
0.2%
2400 7
 
0.2%
Other values (2081) 3247
88.5%
ValueCountFrequency (%)
-24704 1
< 0.1%
-13543 2
0.1%
-9850 2
0.1%
-2760 1
< 0.1%
-2685 1
< 0.1%
-2479 1
< 0.1%
-2086 1
< 0.1%
-2000 1
< 0.1%
-1930 2
0.1%
-1100 2
0.1%
ValueCountFrequency (%)
983931 1
< 0.1%
535509 1
< 0.1%
509229 2
0.1%
506260 2
0.1%
491956 2
0.1%
478380 2
0.1%
475931 1
< 0.1%
470915 1
< 0.1%
458862 2
0.1%
450047 1
< 0.1%

BILL_AMT3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2044
Distinct (%)55.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean45602.332
Minimum-9850
Maximum548020
Zeros384
Zeros (%)10.5%
Negative82
Negative (%)2.2%
Memory size28.8 KiB
2023-12-14T19:59:10.426938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-9850
5-th percentile0
Q12252
median19505
Q356272.75
95-th percentile186452.55
Maximum548020
Range557870
Interquartile range (IQR)54020.75

Descriptive statistics

Standard deviation69402.889
Coefficient of variation (CV)1.5219153
Kurtosis10.55125
Mean45602.332
Median Absolute Deviation (MAD)19115
Skewness2.8652667
Sum1.6726935 × 108
Variance4.8167609 × 109
MonotonicityNot monotonic
2023-12-14T19:59:10.465995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
 
10.5%
390 31
 
0.8%
780 11
 
0.3%
-2 9
 
0.2%
291 9
 
0.2%
316 8
 
0.2%
396 7
 
0.2%
2400 7
 
0.2%
200 6
 
0.2%
-200 6
 
0.2%
Other values (2034) 3190
86.9%
ValueCountFrequency (%)
-9850 2
0.1%
-6144 1
< 0.1%
-3650 1
< 0.1%
-2697 2
0.1%
-2643 1
< 0.1%
-2320 2
0.1%
-2000 1
< 0.1%
-1690 2
0.1%
-1117 1
< 0.1%
-946 2
0.1%
ValueCountFrequency (%)
548020 1
< 0.1%
535020 1
< 0.1%
499936 2
0.1%
479432 2
0.1%
471175 1
< 0.1%
469703 2
0.1%
460317 1
< 0.1%
455286 1
< 0.1%
453770 1
< 0.1%
445129 1
< 0.1%

BILL_AMT4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2008
Distinct (%)54.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean41468.668
Minimum-7905
Maximum891586
Zeros424
Zeros (%)11.6%
Negative84
Negative (%)2.3%
Memory size28.8 KiB
2023-12-14T19:59:10.506637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-7905
5-th percentile0
Q11741.5
median17915
Q349324
95-th percentile168644.4
Maximum891586
Range899491
Interquartile range (IQR)47582.5

Descriptive statistics

Standard deviation67774.637
Coefficient of variation (CV)1.6343577
Kurtosis20.350785
Mean41468.668
Median Absolute Deviation (MAD)17475.5
Skewness3.6105903
Sum1.5210707 × 108
Variance4.5934014 × 109
MonotonicityNot monotonic
2023-12-14T19:59:10.545878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 424
 
11.6%
390 25
 
0.7%
316 15
 
0.4%
291 10
 
0.3%
326 9
 
0.2%
300 8
 
0.2%
2400 7
 
0.2%
416 7
 
0.2%
780 7
 
0.2%
-2 6
 
0.2%
Other values (1998) 3150
85.9%
ValueCountFrequency (%)
-7905 1
< 0.1%
-3684 2
0.1%
-3650 1
< 0.1%
-3450 1
< 0.1%
-2898 2
0.1%
-2618 2
0.1%
-2054 1
< 0.1%
-2000 1
< 0.1%
-1513 1
< 0.1%
-1400 2
0.1%
ValueCountFrequency (%)
891586 1
< 0.1%
628699 2
0.1%
542653 2
0.1%
530672 1
< 0.1%
505507 2
0.1%
487066 2
0.1%
486776 1
< 0.1%
479978 2
0.1%
472621 1
< 0.1%
452162 1
< 0.1%

BILL_AMT5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1983
Distinct (%)54.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean39896.161
Minimum-28335
Maximum927171
Zeros460
Zeros (%)12.5%
Negative87
Negative (%)2.4%
Memory size28.8 KiB
2023-12-14T19:59:10.585897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-28335
5-th percentile0
Q11480.5
median17616
Q348449
95-th percentile168329
Maximum927171
Range955506
Interquartile range (IQR)46968.5

Descriptive statistics

Standard deviation63815.839
Coefficient of variation (CV)1.5995484
Kurtosis20.247809
Mean39896.161
Median Absolute Deviation (MAD)17220
Skewness3.4404962
Sum1.4633912 × 108
Variance4.0724613 × 109
MonotonicityNot monotonic
2023-12-14T19:59:10.626026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 460
 
12.5%
390 28
 
0.8%
316 11
 
0.3%
396 11
 
0.3%
150 8
 
0.2%
2400 7
 
0.2%
2000 7
 
0.2%
780 7
 
0.2%
416 7
 
0.2%
1261 6
 
0.2%
Other values (1973) 3116
84.9%
ValueCountFrequency (%)
-28335 2
0.1%
-10213 1
< 0.1%
-5000 2
0.1%
-3876 1
< 0.1%
-3650 1
< 0.1%
-3450 1
< 0.1%
-3272 2
0.1%
-2946 1
< 0.1%
-2153 1
< 0.1%
-2000 1
< 0.1%
ValueCountFrequency (%)
927171 1
< 0.1%
503914 1
< 0.1%
484993 1
< 0.1%
484612 2
0.1%
483003 2
0.1%
471145 2
0.1%
440982 2
0.1%
392879 1
< 0.1%
392650 1
< 0.1%
377858 1
< 0.1%

BILL_AMT6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1947
Distinct (%)53.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean38776.507
Minimum-339603
Maximum961664
Zeros532
Zeros (%)14.5%
Negative71
Negative (%)1.9%
Memory size28.8 KiB
2023-12-14T19:59:10.668208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q1981.5
median15837.5
Q347550
95-th percentile168108.45
Maximum961664
Range1301267
Interquartile range (IQR)46568.5

Descriptive statistics

Standard deviation64744.765
Coefficient of variation (CV)1.6696905
Kurtosis23.443315
Mean38776.507
Median Absolute Deviation (MAD)15638
Skewness3.4521135
Sum1.4223223 × 108
Variance4.1918845 × 109
MonotonicityNot monotonic
2023-12-14T19:59:10.711455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 532
 
14.5%
390 22
 
0.6%
780 16
 
0.4%
150 13
 
0.4%
316 12
 
0.3%
291 11
 
0.3%
326 11
 
0.3%
396 7
 
0.2%
2500 6
 
0.2%
-2 6
 
0.2%
Other values (1937) 3032
82.6%
ValueCountFrequency (%)
-339603 2
0.1%
-16586 1
< 0.1%
-11060 1
< 0.1%
-4306 1
< 0.1%
-3650 1
< 0.1%
-3614 1
< 0.1%
-3272 2
0.1%
-2946 1
< 0.1%
-2389 1
< 0.1%
-2303 2
0.1%
ValueCountFrequency (%)
961664 1
< 0.1%
699944 1
< 0.1%
527711 1
< 0.1%
496915 1
< 0.1%
473944 2
0.1%
469961 2
0.1%
434715 2
0.1%
419643 2
0.1%
398478 1
< 0.1%
391336 1
< 0.1%

PAY_AMT1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1146
Distinct (%)31.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5573.8424
Minimum0
Maximum239104
Zeros667
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:59:10.751724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2145
Q35006
95-th percentile20000
Maximum239104
Range239104
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation13736.634
Coefficient of variation (CV)2.464482
Kurtosis103.26738
Mean5573.8424
Median Absolute Deviation (MAD)1946
Skewness8.5411615
Sum20444854
Variance1.8869512 × 108
MonotonicityNot monotonic
2023-12-14T19:59:10.792735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 667
 
18.2%
2000 147
 
4.0%
3000 102
 
2.8%
5000 76
 
2.1%
10000 64
 
1.7%
1000 62
 
1.7%
2500 60
 
1.6%
1500 55
 
1.5%
4000 47
 
1.3%
1600 30
 
0.8%
Other values (1136) 2358
64.3%
ValueCountFrequency (%)
0 667
18.2%
1 2
 
0.1%
5 2
 
0.1%
13 2
 
0.1%
20 2
 
0.1%
23 1
 
< 0.1%
27 1
 
< 0.1%
39 4
 
0.1%
44 1
 
< 0.1%
92 2
 
0.1%
ValueCountFrequency (%)
239104 2
0.1%
210000 1
< 0.1%
199646 2
0.1%
163500 1
< 0.1%
160444 1
< 0.1%
140013 1
< 0.1%
120093 2
0.1%
120041 2
0.1%
100000 2
0.1%
90000 2
0.1%

PAY_AMT2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1129
Distinct (%)30.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5131.4654
Minimum0
Maximum285138
Zeros708
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:59:10.831711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1495
median1972.5
Q34913
95-th percentile18112
Maximum285138
Range285138
Interquartile range (IQR)4418

Descriptive statistics

Standard deviation14581.833
Coefficient of variation (CV)2.8416508
Kurtosis134.73837
Mean5131.4654
Median Absolute Deviation (MAD)1943.5
Skewness9.9940173
Sum18822215
Variance2.1262984 × 108
MonotonicityNot monotonic
2023-12-14T19:59:10.869301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 708
 
19.3%
2000 146
 
4.0%
5000 95
 
2.6%
1000 95
 
2.6%
3000 94
 
2.6%
1500 89
 
2.4%
1200 39
 
1.1%
4000 38
 
1.0%
1400 34
 
0.9%
390 34
 
0.9%
Other values (1119) 2296
62.6%
ValueCountFrequency (%)
0 708
19.3%
1 4
 
0.1%
2 4
 
0.1%
3 2
 
0.1%
5 4
 
0.1%
7 3
 
0.1%
8 1
 
< 0.1%
10 2
 
0.1%
11 2
 
0.1%
12 2
 
0.1%
ValueCountFrequency (%)
285138 2
0.1%
199982 2
0.1%
182123 1
< 0.1%
180519 1
< 0.1%
177671 2
0.1%
170000 1
< 0.1%
167622 1
< 0.1%
145000 2
0.1%
129990 1
< 0.1%
121715 1
< 0.1%

PAY_AMT3
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1040
Distinct (%)28.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4723.5365
Minimum0
Maximum896040
Zeros798
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:59:10.913588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1240
median1394.5
Q34000
95-th percentile15310.3
Maximum896040
Range896040
Interquartile range (IQR)3760

Descriptive statistics

Standard deviation19414.123
Coefficient of variation (CV)4.1100822
Kurtosis1228.6441
Mean4723.5365
Median Absolute Deviation (MAD)1394.5
Skewness28.75821
Sum17325932
Variance3.7690819 × 108
MonotonicityNot monotonic
2023-12-14T19:59:10.953291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 798
 
21.7%
1000 172
 
4.7%
2000 152
 
4.1%
3000 100
 
2.7%
5000 82
 
2.2%
1500 55
 
1.5%
4000 48
 
1.3%
10000 44
 
1.2%
6000 29
 
0.8%
2500 29
 
0.8%
Other values (1030) 2159
58.8%
ValueCountFrequency (%)
0 798
21.7%
2 2
 
0.1%
3 2
 
0.1%
4 2
 
0.1%
5 3
 
0.1%
6 1
 
< 0.1%
9 3
 
0.1%
10 3
 
0.1%
12 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
896040 1
< 0.1%
222750 2
0.1%
155000 1
< 0.1%
153400 1
< 0.1%
152618 2
0.1%
148307 1
< 0.1%
133657 2
0.1%
130000 2
0.1%
116446 1
< 0.1%
110699 1
< 0.1%

PAY_AMT4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1034
Distinct (%)28.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4828.5698
Minimum0
Maximum205000
Zeros808
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:59:11.248215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1231.5
median1500
Q34000
95-th percentile17024
Maximum205000
Range205000
Interquartile range (IQR)3768.5

Descriptive statistics

Standard deviation13764.722
Coefficient of variation (CV)2.850683
Kurtosis72.434939
Mean4828.5698
Median Absolute Deviation (MAD)1500
Skewness7.5169861
Sum17711194
Variance1.8946757 × 108
MonotonicityNot monotonic
2023-12-14T19:59:11.288772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 808
 
22.0%
1000 160
 
4.4%
2000 142
 
3.9%
3000 94
 
2.6%
5000 91
 
2.5%
1500 71
 
1.9%
4000 49
 
1.3%
500 41
 
1.1%
2500 37
 
1.0%
10000 36
 
1.0%
Other values (1024) 2139
58.3%
ValueCountFrequency (%)
0 808
22.0%
2 5
 
0.1%
3 1
 
< 0.1%
4 2
 
0.1%
6 6
 
0.2%
7 2
 
0.1%
8 1
 
< 0.1%
10 2
 
0.1%
17 2
 
0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
205000 1
 
< 0.1%
188840 2
0.1%
178460 1
 
< 0.1%
171716 1
 
< 0.1%
161110 1
 
< 0.1%
159212 1
 
< 0.1%
146900 2
0.1%
125009 1
 
< 0.1%
107591 2
0.1%
100000 4
0.1%

PAY_AMT5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1038
Distinct (%)28.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5093.6881
Minimum0
Maximum332000
Zeros827
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:59:11.327719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1214.25
median1500
Q34000
95-th percentile16017.55
Maximum332000
Range332000
Interquartile range (IQR)3785.75

Descriptive statistics

Standard deviation17400.958
Coefficient of variation (CV)3.4161805
Kurtosis137.482
Mean5093.6881
Median Absolute Deviation (MAD)1500
Skewness10.115542
Sum18683648
Variance3.0279335 × 108
MonotonicityNot monotonic
2023-12-14T19:59:11.366866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 827
 
22.5%
1000 163
 
4.4%
2000 123
 
3.4%
3000 119
 
3.2%
5000 77
 
2.1%
1500 73
 
2.0%
4000 48
 
1.3%
2500 33
 
0.9%
500 28
 
0.8%
3500 27
 
0.7%
Other values (1028) 2150
58.6%
ValueCountFrequency (%)
0 827
22.5%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
12 3
 
0.1%
20 2
 
0.1%
24 1
 
< 0.1%
32 2
 
0.1%
ValueCountFrequency (%)
332000 2
0.1%
326889 1
< 0.1%
200000 1
< 0.1%
195599 2
0.1%
184922 2
0.1%
162000 2
0.1%
161000 2
0.1%
160719 2
0.1%
158064 1
< 0.1%
145564 1
< 0.1%

PAY_AMT6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct970
Distinct (%)26.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5188.7181
Minimum0
Maximum528666
Zeros949
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-12-14T19:59:11.407031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1338
Q34000
95-th percentile15709.95
Maximum528666
Range528666
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation20925.17
Coefficient of variation (CV)4.0328207
Kurtosis270.44793
Mean5188.7181
Median Absolute Deviation (MAD)1338
Skewness13.813886
Sum19032218
Variance4.3786272 × 108
MonotonicityNot monotonic
2023-12-14T19:59:11.449125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 949
25.9%
1000 176
 
4.8%
2000 163
 
4.4%
5000 96
 
2.6%
3000 94
 
2.6%
1500 62
 
1.7%
4000 57
 
1.6%
10000 39
 
1.1%
2500 37
 
1.0%
6000 28
 
0.8%
Other values (960) 1967
53.6%
ValueCountFrequency (%)
0 949
25.9%
1 3
 
0.1%
3 3
 
0.1%
4 2
 
0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
12 2
 
0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
528666 2
0.1%
345293 2
0.1%
223833 1
< 0.1%
208896 1
< 0.1%
185652 2
0.1%
175000 1
< 0.1%
173869 1
< 0.1%
171944 2
0.1%
167000 2
0.1%
159753 1
< 0.1%
Distinct2
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size28.8 KiB
1.0
2873 
0.0
795 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11004
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2873
78.3%
0.0 795
 
21.7%
(Missing) 1
 
< 0.1%

Length

2023-12-14T19:59:11.487456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T19:59:11.519400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2873
78.3%
0.0 795
 
21.7%

Most occurring characters

ValueCountFrequency (%)
0 4463
40.6%
. 3668
33.3%
1 2873
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7336
66.7%
Other Punctuation 3668
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4463
60.8%
1 2873
39.2%
Other Punctuation
ValueCountFrequency (%)
. 3668
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4463
40.6%
. 3668
33.3%
1 2873
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4463
40.6%
. 3668
33.3%
1 2873
26.1%

Interactions

2023-12-14T19:59:08.324402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:53.596495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.471410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.263559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.997565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.788732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.487027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.346794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.045751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.754535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.489701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.367556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.107343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.823498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:03.545256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.469708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.203381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.942813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.663291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.570381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.361648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:53.676232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.509805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.299492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.033224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.825411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.523921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.383511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.081649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.791825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.528495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.407033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.143341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.859367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:03.747005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.506831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.239840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.981141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.699286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.609609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.398168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:53.756303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.544780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.334249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.069915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.859613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.558352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.417876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.116086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.828881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.566097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.442998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.178290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.895109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:03.783838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.543427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.280578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.016659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.734450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.647631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.434021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:53.818803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.579708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.369237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.105056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.893500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.592121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.451562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.150675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.866330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.740273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.479681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.213267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.932654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:03.821506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.578937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.317973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.052899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.768846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.684093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.468337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:53.877036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.613236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.402496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.138357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.926336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.624755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.484187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.183952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.902097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.775860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.515003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.246856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.968018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:03.857871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.613792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.354399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.087182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.803683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.720145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.505481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:53.912379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.647043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.437791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.171782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.959777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.659202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.516884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.217338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.937017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.810657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.551133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-12-14T19:59:03.364078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.275191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.017945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.760877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.482135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.390003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.135615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.911850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.318966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.119848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.849847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.649394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.342890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.094405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.905664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.611195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.343658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.222420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.956778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.676122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:03.401249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.315500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.054272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.797933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.517848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.427333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.172879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.955788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.356137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.154217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.885941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.683118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.377498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.129636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.939403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.646051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.378969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.257680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.992682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.713729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:03.436304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.353141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.091881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.833191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.552091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.462687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.209653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:09.000675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.396355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.188831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.923004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.716295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.412850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.163552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.972602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.682117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.415039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.292784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.028260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.749698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:03.470866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.390025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.127750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.868221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.587143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.497116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.246205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:09.044409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:54.434464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.227094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:55.962128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:56.752734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:57.449310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:58.201426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.008793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:58:59.718689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:00.453280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:01.330567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.070314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:02.787233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:03.507800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:04.431595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.165597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:05.905716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:06.627083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:07.534340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-14T19:59:08.286514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-12-14T19:59:11.561300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6SEXEDUCATIONMARRIAGEdefault payment next month
LIMIT_BAL1.0000.218-0.209-0.295-0.286-0.271-0.269-0.2620.0750.0870.0950.0920.0900.0920.2960.2720.2820.2470.3000.3200.0900.1360.0940.062
AGE0.2181.000-0.043-0.072-0.076-0.062-0.056-0.0530.0110.0100.0150.0110.0080.0080.0460.0810.0430.0150.0410.0510.1070.1840.3070.077
PAY_0-0.209-0.0431.0000.5610.4980.4460.4250.4160.2760.2960.2870.2860.2830.291-0.105-0.062-0.039-0.012-0.020-0.0240.0540.1050.0390.376
PAY_2-0.295-0.0720.5611.0000.7980.7050.6910.6620.5830.5670.5350.5340.5070.4880.0480.1080.1440.1230.1030.1310.0630.1260.0390.276
PAY_3-0.286-0.0760.4980.7981.0000.8080.7290.6910.5420.6000.5770.5620.5390.5180.2500.0630.1420.1560.1470.1520.0640.1200.0400.263
PAY_4-0.271-0.0620.4460.7050.8081.0000.8340.7540.5310.5630.6460.6390.6010.5730.1890.2690.1250.1800.1760.2050.0580.1080.0440.225
PAY_5-0.269-0.0560.4250.6910.7290.8341.0000.8200.5150.5400.6070.6690.6300.5870.1640.2460.2940.1260.1920.2160.0620.0950.0440.253
PAY_6-0.262-0.0530.4160.6620.6910.7540.8201.0000.5160.5400.5930.6300.6830.6520.1660.2320.2600.3170.1600.2390.0730.1100.0340.183
BILL_AMT10.0750.0110.2760.5830.5420.5310.5150.5161.0000.9130.8600.8150.7790.7540.5170.4770.4880.4550.4380.4410.0730.0350.0200.032
BILL_AMT20.0870.0100.2960.5670.6000.5630.5400.5400.9131.0000.9010.8480.8020.7780.6510.4910.5060.4660.4540.4660.0820.0410.0000.024
BILL_AMT30.0950.0150.2870.5350.5770.6460.6070.5930.8600.9011.0000.9080.8560.8210.5420.6370.5290.5000.4910.5040.0570.0760.0000.036
BILL_AMT40.0920.0110.2860.5340.5620.6390.6690.6300.8150.8480.9081.0000.8970.8450.4980.5640.6500.5080.4940.5160.0380.0370.0250.036
BILL_AMT50.0900.0080.2830.5070.5390.6010.6300.6830.7790.8020.8560.8971.0000.8910.4660.5250.5560.6620.5040.5410.0440.0260.0390.032
BILL_AMT60.0920.0080.2910.4880.5180.5730.5870.6520.7540.7780.8210.8450.8911.0000.4520.5030.5250.5750.6690.5450.0580.0420.0100.058
PAY_AMT10.2960.046-0.1050.0480.2500.1890.1640.1660.5170.6510.5420.4980.4660.4521.0000.4710.5120.4530.4710.4550.0000.0620.0170.062
PAY_AMT20.2720.081-0.0620.1080.0630.2690.2460.2320.4770.4910.6370.5640.5250.5030.4711.0000.5340.5290.5020.5080.0290.0550.0000.054
PAY_AMT30.2820.043-0.0390.1440.1420.1250.2940.2600.4880.5060.5290.6500.5560.5250.5120.5341.0000.5100.5170.5190.0280.0540.0000.000
PAY_AMT40.2470.015-0.0120.1230.1560.1800.1260.3170.4550.4660.5000.5080.6620.5750.4530.5290.5101.0000.5130.5450.0210.0630.0000.032
PAY_AMT50.3000.041-0.0200.1030.1470.1760.1920.1600.4380.4540.4910.4940.5040.6690.4710.5020.5170.5131.0000.5430.0700.0330.0000.037
PAY_AMT60.3200.051-0.0240.1310.1520.2050.2160.2390.4410.4660.5040.5160.5410.5450.4550.5080.5190.5450.5431.0000.0290.0730.0000.041
SEX0.0900.1070.0540.0630.0640.0580.0620.0730.0730.0820.0570.0380.0440.0580.0000.0290.0280.0210.0700.0291.0000.7080.7080.000
EDUCATION0.1360.1840.1050.1260.1200.1080.0950.1100.0350.0410.0760.0370.0260.0420.0620.0550.0540.0630.0330.0730.7081.0000.5120.060
MARRIAGE0.0940.3070.0390.0390.0400.0440.0440.0340.0200.0000.0000.0250.0390.0100.0170.0000.0000.0000.0000.0000.7080.5121.0000.037
default payment next month0.0620.0770.3760.2760.2630.2250.2530.1830.0320.0240.0360.0360.0320.0580.0620.0540.0000.0320.0370.0410.0000.0600.0371.000

Missing values

2023-12-14T19:59:09.130184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-14T19:59:09.249246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-14T19:59:09.391400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
120000.0femaleuniversity124.02.02.0-1.0-1.0-2.0-2.03913.03102.0689.00.00.00.00.0689.00.00.00.00.00.0
2120000.0femaleuniversity226.0-1.02.00.00.00.02.02682.01725.02682.03272.03455.03261.00.01000.01000.01000.00.02000.00.0
390000.0femaleuniversity234.00.00.00.00.00.00.029239.014027.013559.014331.014948.015549.01518.01500.01000.01000.01000.05000.01.0
450000.0femaleuniversity137.00.00.00.00.00.00.046990.048233.049291.028314.028959.029547.02000.02019.01200.01100.01069.01000.01.0
550000.0maleuniversity157.0-1.00.0-1.00.00.00.08617.05670.035835.020940.019146.019131.02000.036681.010000.09000.0689.0679.01.0
650000.0malegraduate school237.00.00.00.00.00.00.064400.057069.057608.019394.019619.020024.02500.01815.0657.01000.01000.0800.01.0
7500000.0malegraduate school229.00.00.00.00.00.00.0367965.0412023.0445007.0542653.0483003.0473944.055000.040000.038000.020239.013750.013770.01.0
8100000.0femaleuniversity223.00.0-1.0-1.00.00.0-1.011876.0380.0601.0221.0-159.0567.0380.0601.00.0581.01687.01542.01.0
9140000.0femalehigh school128.00.00.02.00.00.00.011285.014096.012108.012211.011793.03719.03329.00.0432.01000.01000.01000.01.0
1020000.0malehigh school235.0-2.0-2.0-2.0-2.0-1.0-1.00.00.00.00.013007.013912.00.00.00.013007.01122.00.01.0
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
3660380000.0maleuniversity150.00.00.00.00.00.00.0385662.0294826.0220022.0154283.035270.0332270.012020.09009.06109.03000.0332000.012000.00.0
366150000.0maleuniversity144.00.00.00.00.00.00.045335.046027.030286.026275.026823.027371.01524.01427.0941.0972.0992.01000.01.0
3662150000.0femalehigh school143.0-1.0-1.02.00.0-1.0-1.0264.0948.0632.0316.0316.01414.01000.00.00.0316.01414.00.00.0
3663220000.0maleuniversity229.00.00.00.00.00.00.0122286.0122839.0123035.0114385.0115903.0118528.05008.05007.06007.05000.04700.05503.01.0
366480000.0femaleother227.00.00.00.00.00.00.045268.047140.047411.048443.049478.043264.02600.01800.01700.01700.01700.01300.01.0
3665220000.0femaleuniversity132.00.00.00.00.00.00.0194961.0197536.0203251.0208355.0213015.0217475.07200.09000.010000.08000.08010.08500.01.0
366670000.0femaleuniversity234.01.02.02.02.00.00.024208.025015.027189.026456.028361.031873.01500.02900.00.02500.04000.00.01.0
3667120000.0maleuniversity237.0-1.02.00.00.00.02.016241.016680.017695.017901.019608.019143.01000.01600.0800.02000.00.01600.00.0
3668180000.0femaleuniversity232.00.00.00.00.00.00.020730.017107.035884.031057.029052.025933.01582.030000.01000.01000.01000.01000.01.0
366950000.0femalehigh school157.00.00.00.00.00.00.049017.050690.047487.048319.048449.049656.02500.02000.02000.01746.02000.01800.01.0

Duplicate rows

Most frequently occurring

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month# duplicates
010000.0femalehigh school222.00.00.00.00.0-2.0-2.08109.09778.08259.00.00.00.02000.01036.00.00.00.00.00.02
110000.0femaleuniversity131.00.00.00.00.00.00.015915.09050.09901.09975.09736.08703.02330.02200.01000.0333.0311.0322.01.02
210000.0femaleuniversity222.01.02.00.00.00.00.010250.08558.010525.010050.09903.09984.00.02126.0390.0328.0476.01287.01.02
310000.0malehigh school223.00.00.00.00.00.02.06974.07838.09002.09182.09729.09411.01134.01298.0478.0847.00.0175.01.02
410000.0malehigh school235.00.00.00.00.00.00.07877.08918.09864.09673.09414.09156.01174.01120.0310.0316.01000.02000.01.02
510000.0maleuniversity132.01.02.02.02.02.02.08425.08148.09481.09180.010052.010091.00.01632.00.01022.0350.00.01.02
610000.0maleuniversity145.00.00.00.02.00.00.07139.08416.09815.09508.09754.010192.01400.01700.00.0400.0600.0200.00.02
710000.0maleuniversity156.02.02.02.00.00.00.02097.04193.03978.04062.04196.04326.02300.00.0150.0200.0200.0160.00.02
810000.0maleuniversity222.00.00.00.00.00.00.01877.03184.06003.03576.03670.04451.01500.02927.01000.0300.01000.0500.01.02
910000.0maleuniversity222.00.00.00.00.00.00.07960.09649.08518.08628.09293.05033.02000.01000.0500.01500.00.02500.00.02